Dysregulation of Immune Cell Subpopulations in Atypical Hemolytic Uremic Syndrome

Atypical hemolytic uremic syndrome (aHUS) is a rare, life-threatening thrombotic microangiopathy. Definitive biomarkers for disease diagnosis and activity remain elusive, making the exploration of molecular markers paramount. We conducted single-cell sequencing on peripheral blood mononuclear cells from 13 aHUS patients, 3 unaffected family members of aHUS patients, and 4 healthy controls. We identified 32 distinct subpopulations encompassing 5 B-cell types, 16 T- and natural killer (NK) cell types, 7 monocyte types, and 4 other cell types. Notably, we observed a significant increase in intermediate monocytes in unstable aHUS patients. Subclustering analysis revealed seven elevated expression genes, including NEAT1, MT-ATP6, MT-CYB, VIM, ACTG1, RPL13, and KLRB1, in unstable aHUS patients, and four heightened expression genes, including RPS27, RPS4X, RPL23, and GZMH genes, in stable aHUS patients. Additionally, an increase in the expression of mitochondria-related genes suggested a potential influence of cell metabolism on the clinical progression of the disease. Pseudotime trajectory analysis revealed a unique immune cell differentiation pattern, while cell—cell interaction profiling highlighted distinctive signaling pathways among patients, family members, and controls. This single-cell sequencing study is the first to confirm immune cell dysregulation in aHUS pathogenesis, offering valuable insights into molecular mechanisms and potential new diagnostic and disease activity markers.


Introduction
Atypical hemolytic uremic syndrome (aHUS) is a rare and life-threatening thrombotic microangiopathy (TMA) disease characterized by a triad of microangiopathic hemolytic anemia, thrombocytopenia, and acute kidney injury. This presentation is distinct from thrombotic thrombocytopenic purpura and other TMA diseases. The TMA of aHUS affects multiple organ systems, often leading to rapid multi-organ failure and mortality [1]. The pathogenesis of aHUS is closely related to the dysregulation of the complement system, which normally functions to protect the body against invading pathogens that can damage cells. In aHUS, alternative complement pathway gene mutations or the dysregulation of complement regulators can lead to the excessive activation of the complement system after the body's environment has come into contact with a range of triggers, including

The Immunological Landscape of Immune Cells from aHUS, aHUS Family, and Healthy
We conducted scRNA-seq on PBMCs from aHUS patients (N = 13), family members (N = 3), and healthy controls (N = 4) to examine immune cell heterogeneity in aHUS ( Figure 1a). After preprocessing and quality control, we obtained single-cell transcriptomes of 112,191, 24,848, and 37,539 immune cells from aHUS patients, family members, and healthy controls, respectively. This enabled distinguishing among groups, disease activity, and treatments (plasma exchange only, combined anti-complement therapy).
Using SCTransform normalization and robust principal component analysis (rPCA) in Seurat, we identified 32 PBMC cell subpopulations in aHUS patients. SingleR annotation predicted B-cells, T-cells, monocytes, macrophages, dendritic cells, NK cells, megakaryocytes, granulocytes, and progenitors ( Figure 1b). The analysis of five B-cell subpopulations displayed high diversity in aHUS patients and families, influenced by disease activity and treatment (Figure 2a    Non-switch memory B-cells and plasmacytoid dendritic cells were more abundant in controls than in aHUS patients and families (p < 0.05, Figure 5j,k). Intermediate and classical monocytes were higher in patients compared to families (p < 0.05, Figure 5l,m), while non-classical monocytes were lower in patients compared to families (p < 0.05, Figure 5n).

Comparing Stable and Unstable aHUS Patients, aHUS Family, and Healthy Controls
Intermediate monocytes were significantly enriched in the unstable aHUS group, followed by the stable group, aHUS family, and the controls (p < 0.05, Figure 6a). Conversely, classical monocytes were enriched in the stable group compared to the unstable group (p < 0.05, Figure 6b). Plasmablasts, non-Vd2 gd T-cells, and effector memory CD8 T-cells increased in the unstable group, followed by the stable group, aHUS family, and controls,  Plasmacytoid dendritic cells were more abundant in the healthy group, followed by the aHUS family, stable group, and unstable group (p < 0.05, Figure 6f), with the unstable group showing significantly lower levels compared to the control group. For non-switched B-cells, the stable group had significantly lower levels compared to the control group (p < 0.05, Figure 6g).

Comparing Different Treatment in aHUS Patients, aHUS Family, and Healthy Controls
In this subgroup analysis of aHUS treatment, intermediate monocyte enrichment showed an increasing trend from the plasma exchange group to the combined plasma exchange with anti-complement therapy group, aHUS family group, and healthy control group (Figure 7a). The difference was only statistically significant (p < 0.05) between plasma exchange and healthy control groups, with no significant difference between the two treatment groups. Plasmacytoid dendritic cell abundance exhibited an increasing trend from the healthy control group to the aHUS family group, followed by the combined therapy group and plasma exchange group, with the highest levels occurring in the controls and the lowest levels in the plasma exchange group (Figure 7b). The combined therapy group exhibited significant enrichment of follicular helper T-cells, Th1/Th17 cells, and Th17 cells compared to the plasma exchange group (Figure 7c    Non-switch memory B-cells and plasmacytoid dendritic cells were more abundant in controls than in aHUS patients and families (p < 0.05, Figure 5j,k). Intermediate and classical monocytes were higher in patients compared to families (p < 0.05, Figure 5l,m), while non-classical monocytes were lower in patients compared to families (p < 0.05, Figure 5n).

Comparing Stable and Unstable aHUS Patients, aHUS Family, and Healthy Controls
Intermediate monocytes were significantly enriched in the unstable aHUS group, followed by the stable group, aHUS family, and the controls (p < 0.05, Figure 6a). Conversely, classical monocytes were enriched in the stable group compared to the unstable group (p < 0.05, Figure 6b). Plasmablasts, non-Vd2 gd T-cells, and effector memory CD8 T-cells increased in the unstable group, followed by the stable group, aHUS family, and controls, with significant differences between the control and unstable groups (p < 0.05, Figure 6ce).
Plasmacytoid dendritic cells were more abundant in the healthy group, followed by the aHUS family, stable group, and unstable group (p < 0.05, Figure 6f), with the unstable group showing significantly lower levels compared to the control group. For nonswitched B-cells, the stable group had significantly lower levels compared to the control group (p < 0.05, Figure 6g).

Comparing aHUS Patients, aHUS Family, and Healthy Controls
This study identified significant differences in immune cell subclusters among aHUS patients, aHUS family members, and healthy controls. In aHUS patients compared to healthy controls, we observed increased levels of classical monocytes (subclusters 6, 7) with higher RPS27 and IFI27 expression (Figure 8a
In pseudotimes 0-5, naïve CD8 T-cell abundance was highest in the healthy controls, followed by aHUS families and patients (Figure 10c). This trend reversed in pseudotimes 5-10. No significant differences in plasmacytoid dendritic cells, myeloid dendritic cells, non-classical monocytes, and classical monocytes were observed among aHUS patients, families, and healthy controls in pseudotimes 0-30. However, intermediate monocyte abundance in aHUS patients significantly increased during pseudotimes 7-10, which was not observed in the families or healthy controls (Figure 10d).
Cytopath analysis showed immune cell state dynamics in B-cell, T-cell, and monocyte trajectories (Figure 9). Naïve B-cells, exhausted B-cells, and non-switched memory B-cells in the aHUS group peaked at pseudotimes 0, 9, and 12, differing from healthy controls and aHUS nuclear families. From pseudotimes 5 to 10, naïve B-cell abundance was highest in the healthy controls, followed by aHUS families, and was lowest in the aHUS group (Figure 10a).

Comparing Stable and Unstable aHUS Patients, aHUS Family, and Healthy Controls
The unstable aHUS group showed a significant increase in exhausted B-cells during pseudotimes 7-13 compared to stable aHUS, aHUS families, and healthy controls, followed by a decline from pseudotimes 13-18. In this interval, switched memory B-cell abundance was lowest in the unstable group compared to the others. Non-switched memory B-cells were more abundant in the unstable group, with the largest difference at pseudotimes 10-15 (Figure 10e,f).  Th2, Th17, and Th1/Th17 cells had the lowest abundance in the unstable aHUS group at pseudotimes 18-22 but peaked at pseudotimes 28-32 for Th2, Th17, Th1/Th17, Th1, T regulatory, follicular helper T, and naïve CD4 T-cells (Figure 10f). The stable aHUS group showed a pattern more akin to the unstable group than to aHUS families and healthy controls.   APP-D40 interactions in aHUS patients were divided into two patterns, with pattern 2 being similar to aHUS family and healthy controls. Classical monocytes, intermediate monocytes, megakaryocytes, and myeloid dendritic cells demonstrated increased outgoing signaling in pattern 1.
IL16-CD4 interactions in aHUS patients had two patterns. Outgoing signaling from myeloid dendritic cells and plasma blasts was highest in pattern 1, while intermediate monocytes, myeloid dendritic cells, non-classical monocytes, and classical monocytes exhibited increased incoming signaling.
CD86-CTLA4 pathways in aHUS patients displayed distinct patterns with heightened outgoing signaling in non-classical monocytes. In the SELPLG pathway, cases a7 and a10 showed significantly increased outgoing signaling in megakaryocytes. The CXC interactions in aHUS patients had three patterns. Patterns 1 and 2 demonstrated significantly reduced incoming signaling from non-switched memory B-cells, central memory CD8 T-cells, Vd2 gd T-cells, Th1/Th17 cells, and Th1 cells compared to pattern 3 and the healthy control. Notably, case a10 exhibited significantly increased outgoing signaling from non-classical monocytes and incoming signaling from MAIT cells, which was unobserved in other participants.

Discussion
aHUS represents a rare, life-threatening condition. The challenges in diagnosing and managing aHUS largely stem from the absence of specific diagnostic markers and the disease's rapid progression. In a pioneering effort, our study employs single-cell sequencing to probe immune cell dysregulation in aHUS, thereby offering unique insights into the disease's pathogenesis and its clinical implications.
In our study, we analyzed cell subpopulations and found that aHUS patients had higher levels of plasmablasts, intermediate monocytes, terminal effector CD4 T-cells, Th1 cells, effector memory CD8 T-cells, and terminal effector CD8 T-cells compared to aHUS families and healthy controls. In contrast, non-switch memory B-cells and plasmacytoid dendritic cells were most abundant in healthy controls, followed by aHUS families and patients. The unstable aHUS group showed significantly higher intermediate monocyte abundance than stable aHUS, aHUS families, and healthy controls. We suggest intermediate monocytes as potential aHUS disease activity markers. In the study conducted by Zawada AM et al., the pivotal role of monocytes is examined, where they are segmented into three distinct subsets with a particular emphasis on the potential involvement of the intermediate subset in atherosclerosis [17]. In the research presented by Wong KL et al., the authors delineate the specific phenotypes and functions of these monocyte subsets and further discuss alternative markers for their segregation [18]. Finally, Ziegler-Heitbrock L. et al. propose an officially endorsed classification for monocyte and dendritic cell subsets in their study, aiming to streamline communication and spur further research within the scientific community [19]. These monocytes also interact with endothelial cells, indicating a potential contribution to aHUS pathogenesis and correlation with endothelial cells.
In the study led by Perez RK et al., they identify a heightened expression of type 1 interferon-stimulated genes in monocytes, a decrease in naive CD4+ T-cells that was aligned with the upregulated monocyte ISG expression, and an expansion of cytotoxic GZMH+ CD8+ T-cells with limited repertoire diversity [20]. In the study led by Nehar-Belaid D et al., they notice the expansion of unique interferon-stimulated genes and/or monogenic lupusassociated gene-enriched subpopulations which could identify patients with the highest disease activity [21]. Li Y et al. demonstrates that a six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) offers valuable diagnostic utility for systemic lupus erythematosus (SLE) [22]. Zhang Y et al. further substantiate this by observing increased levels of macrophage migration inhibitory factor (MIF) in the serum of SLE patients [23]. Shifting focus to IgG4-related disease (IgG4-RD), Wu X et al. identify increased proportions of CD8 central memory T-(TCM) and TIGIT+ CD8 cytotoxic T (CTL)-cells in patients compared to the healthy controls. Their additional analysis illuminates the critical role of B-cell activation factor (BAFF) signaling pathways, showing their enrichment from myeloid cell subsets to B-cells [24].
We also observed distinct gene expression patterns between unstable and stable aHUS. Unstable aHUS exhibited increased expression of NEAT1, MT-ATP6, MT-CYB, VIM, ACTG1, RPL13, and KLRB1 genes in various immune cell subclusters, while stable aHUS showed upregulated RPS27, RPS4X, RPL23, and GZMH genes. These genes may serve as potential clinical markers for aHUS disease activity. Elevated mitochondria-related gene expression suggests cell metabolism's role in the aHUS clinical course, warranting further investigation. Notably, these gene expression patterns were not observed in other autoimmune diseases, such as systemic lupus erythematosus [20][21][22][23] or immunoglobulin G4-related disease [24], highlighting the unique immune cell profile in aHUS.
Our pseudotime trajectory analysis revealed a unique point where immune cell differentiation in aHUS patients diverged from healthy individuals. This divergence was also apparent in some aHUS family members, falling between aHUS patients and healthy controls.
In our cell-cell interaction analysis, we aimed to identify signaling pathway differences between healthy individuals and aHUS patients, ensuring observed complement and immune cell interactions were not comparable between groups. The results showed unique signaling patterns in aHUS patients, specifically in ALCAM-CD6, IL16-CD4, APP-CD40, CD86-CTLA4, CXC, and SELPLG pathways, indicating distinctions from healthy individuals. Moreover, MIF or BAFF pathways, common in SLE and IgG4-related diseases, were not increased.
Our study underscores several statistically significant differences, yet the rarity of aHUS and the consequent limitation in case enrollment necessitates future investigations involving a broader participant base. A pertinent consideration is that aHUS's incidence does not display a direct correlation with factors such as age or gender. However, due to funding constraints, our selection of healthy controls was restricted to four individuals and three unaffected family members. This sampling constraint may potentially introduce a selection bias, which we recognize as a limitation of our current study. We will explicitly address this point in the limitations section of our paper. Going forward, we aim to undertake more exhaustive and far-reaching studies to mitigate this issue and enhance the robustness of our findings.
The sensitivity of our scRNA-seq method, influenced by protocol specifics and data quality, generally detects thousands of genes per cell, but is limited in discerning lowly expressed genes. The complementary methods used, CellChat and Monocle 3, similarly rely on data quality and dataset characteristics. Statistical test sensitivity is tied to sample size and effect size. Despite their recognized limitations, we deemed these methods suitably sensitive for our research, bolstered by rigorous protocol adherence and multifaceted validation to ensure finding robustness and reliability.

Patient Recruitment
In this single-center Taiwanese study, peripheral blood samples from 13 adult aHUS patients, 3 unaffected family members, and 4 healthy subjects were analyzed using scRNA-seq. aHUS patients were classified into stable and unstable groups, receiving plasma exchange alone or combined with anti-complement therapy. Stable disease had stable TMA-related organ involvement and normal hemolysis markers.
Three individuals were unaffected family members who were directly blood-related to our aHUS patients. It was validated that they neither exhibited any clinical symptoms of aHUS nor demonstrated any anomalies in their hemolysis markers. Further supplementing our control cohort were four healthy medical professionals who willingly participated as 'healthy controls'. Everyone underwent rigorous health evaluations, ensuring not only the absence of abnormalities in their hemolysis markers, but also confirming no personal or familial history of aHUS.

Single Cell RNA-Seq Data Integration and Clustering
Using the SCTransform workflow, scRNA-seq datasets were integrated, scaled, and normalized, considering regression variables such as cell cycle stage, mitochondrial reads, gene number, and UMI count. The top 3000 variable genes were selected for PCA using SelectIntegrationFeatures. A reference-based integration workflow with rPCA was applied, using four healthy control samples as reference. The top 50 PCs from PCA were used for UMAP, and the FindNeighbors function constructed a nearest neighbors graph for clustering analysis, all provided by the Seurat v4.0.4 package.

Cell Type Annotations
Using SingleR v1.4.1 [29], a reference-based cell type annotation tool, cell types in the dataset were classified by comparing gene expression profiles and assigning nomenclature and cell ontology terms. Reference gene expression data were obtained from five functions provided by the celldex v1.0.0 R package. MonacoImmuneData labels were selected first, followed by Macrophages M1 and M2 labels from BlueprintEncodeData. Lastly, cell types such as Macrophages (CL: 0000235), Lung Macro (CL: 0000583), INF-Macro (CL: 0000863), and Megakaryocyte (CL: 0000556) were identified using cell ontology terms.

Clustering Analysis
Further clustering analysis was performed using the same parameters as before, with a resolution range of 0.1 to 0.5. The Seurat function FindAllMarkers was applied to identify expression markers for each cluster in each cell type.

Pseudotime Estimation
Monocle 3 [30] was utilized to construct cell trajectory paths for B-cells, CD4+ T-cells, CD8+ T-cells, and monocyte lineages. This involved dimensionality reduction using PCA and UMAP, followed by Leiden clustering [31]. Trajectory paths were built by connecting nearest neighbors in the UMAP graph, with the root node determined based on early-stage cell types, such as naïve B-cells, naïve T-cells, and monocytes. Cell type abundance along pseudotime from the trajectory path was also analyzed.

Cell-Cell Communication Analysis
The CellChat v1.1.3 [32] R package was used to infer the probability of ligand-receptor signaling communication among all cell types in scRNA-seq datasets. Heatmaps of cell-cell interaction probability were generated for each sample using the ComplexHeatmap [33] R package and visualized with Morpheus (https://software.broadinstitute.org/morpheus, accessed on 5 February 2023) for each signaling pathway.

Ethics Approval and Consent to Participate
In accordance with ethical standards, we obtained informed consent from all participants. This study received approval from the Research Ethics Committee of China Medical University Hospital, Taiwan (CMUH111-REC2-048). Additionally, all procedures adhered to both the principles of the Declaration of Helsinki and the Good Clinical Practice Guidelines. Every participant provided their explicit informed consent before partaking in this study.

Conclusions
Our study presents robust evidence underscoring the critical role of immune cell dysregulation in the pathogenesis of atypical hemolytic uremic syndrome (aHUS). We reveal intermediate monocytes as a novel potential disease activity marker, displaying significant abundance in unstable aHUS cases. Furthermore, our investigation into immune cell subclusters exposes distinct gene expression profiles between stable and unstable aHUS, thus offering potential clinical indicators for assessing aHUS disease activity. Our pseudotime trajectory analysis uncovers a divergence point in immune cell differentiation between aHUS patients and healthy controls, highlighting a previously unobserved pathogenetic mechanism. Complementing this, our cell-cell interaction analysis discloses striking differences in signaling pathways between aHUS patients and healthy individuals, suggesting altered intercellular communication as a crucial player in aHUS. Taken together, these findings significantly enrich our understanding of the molecular mechanisms underpinning aHUS and hold the potential to catalyze the development of novel diagnostic tools and disease activity markers. We posit that this study paves the way for future investigations into the pathogenesis of aHUS and the development of innovative therapeutic strategies for this complex disease.